Correct co2_eq_emissions in README.md as it should be given in grams instead of kg. (#2)

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	---
	license: bigscience-bloom-rail-1.0
	datasets:
	- nicholasKluge/instruct-aira-dataset
	language:
	- pt
	metrics:
	- accuracy
	library_name: transformers
	tags:
	- alignment
	- instruction tuned
	- text generation
	- conversation
	- assistant
	pipeline_tag: text-generation
	widget:
	- text: "<\|startofinstruction\|>Me explique o que é Aprendizagem de Máquina?<\|endofinstruction\|>"
	example_title: Aprendizagem de Máquina
	- text: "<\|startofinstruction\|>Você sabe alguma coisa sobre a Ética das Virtudes?<\|endofinstruction\|>"
	example_title: Ética
	- text: "<\|startofinstruction\|>Como eu posso fazer a minha namorada feliz?<\|endofinstruction\|>"
	example_title: Conselho
	inference:
	parameters:
	repetition_penalty: 1.2
	temperature: 0.2
	top_k: 30
	top_p: 0.3
	max_new_tokens: 100
	length_penalty: 0.3
	early_stopping: true
	co2_eq_emissions:
	emissions: 1990
	source: CodeCarbon
	training_type: fine-tuning
	geographical_location: Singapore
	hardware_used: NVIDIA A100-SXM4-40GB
	---
	# Aira-2-portuguese-1B7

	Aira-2 is the second version of the Aira instruction-tuned series. Aira-2-portuguese-1B7 is an instruction-tuned model based on [BLOOM](https://huggingface.co/bigscience/bloom-1b7). The model was trained with a dataset composed of prompts and completions generated synthetically by prompting already-tuned models (ChatGPT, Llama, Open-Assistant, etc).

	Check our gradio-demo in [Spaces](https://huggingface.co/spaces/nicholasKluge/Aira-Demo-Portuguese).

	## Details

	- Size: 1,722,005,504 parameters
	- Dataset: [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset)
	- Language: Portuguese
	- Number of Epochs: 3
	- Batch size: 4
	- Optimizer: `torch.optim.AdamW` (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
	- GPU: 1 NVIDIA A100-SXM4-40GB
	- Emissions: 1.99 KgCO2 (Singapore)
	- Total Energy Consumption: 4.09 kWh

	This repository has the [source code](https://github.com/Nkluge-correa/Aira) used to train this model.

	## Usage

	Three special tokens are used to mark the user side of the interaction and the model's response:

	`<\|startofinstruction\|>`O que é um modelo de linguagem?`<\|endofinstruction\|>`Um modelo de linguagem é uma distribuição de probabilidade sobre um vocabulário.`<\|endofcompletion\|>`

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

	tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-portuguese-1B7')
	aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-portuguese-1B7')

	aira.eval()
	aira.to(device)

	question = input("Enter your question: ")

	inputs = tokenizer(tokenizer.bos_token + question + tokenizer.sep_token,
	add_special_tokens=False,
	return_tensors="pt").to(device)

	responses = aira.generate(**inputs,
	do_sample=True,
	top_k=50,
	top_p=0.95,
	temperature=0.7,
	num_return_sequences=2)

	print(f"Question: 👤 {question}\n")

	for i, response in enumerate(responses):
	print(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, "")}')
	```

	The model will output something like:

	```markdown
	>>> Question: 👤 Qual a capital da Alemanha?

	>>>Response 1: 🤖 A capital da Alemanha é Berlim. É a maior cidade da Alemanha e serve como centro administrativo, cultural e político da Alemanha.
	>>>Response 2: 🤖 A capital da Alemanha é Berlim. É a maior cidade da Alemanha e serve como centro administrativo, cultural e político da Alemanha.
	```

	## Limitations

	- Hallucinations: This model can produce content that can be mistaken for truth but is, in fact, misleading or entirely false, i.e., hallucination.

	- Biases and Toxicity: This model inherits the social and historical stereotypes from the data used to train it. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities.

	- Repetition and Verbosity: The model may get stuck on repetition loops (especially if the repetition penalty during generations is set to a meager value) or produce verbose responses unrelated to the prompt it was given.

	## Cite as 🤗

	```latex

	@misc{nicholas22aira,
	doi = {10.5281/zenodo.6989727},
	url = {https://huggingface.co/nicholasKluge/Aira-2-portuguese-1B7},
	author = {Nicholas Kluge Corrêa},
	title = {Aira},
	year = {2023},
	publisher = {HuggingFace},
	journal = {HuggingFace repository},
	}

	```

	## License

	Aira-2-portuguese-1B7 is licensed under the RAIL License since it is a model derived from BLOOM. See the [LICENSE](LICENSE) file for more details.